Ma Lijia, Gong Maoguo, Cai Qing, Jiao Licheng
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi'an, Shaanxi Province 710071, China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Aug;88(2):022810. doi: 10.1103/PhysRevE.88.022810. Epub 2013 Aug 20.
The community structure and the robustness are two important properties of networks for analyzing the functionality of complex systems. The community structure is crucial to understand the potential functionality of complex systems, while the robustness is indispensable to protect the functionality of complex systems from malicious attacks. When a network suffers from an unpredictable attack, its structural integrity would be damaged. Earlier studies focused on the integrity of the node structure or the edge structure when a network suffers from a single-level malicious attack on the nodes or the edges. In this study, we model the attack on the network as a two-level targeted one. Then, we propose a community robustness index to evaluate the integrality of the community structure when the network suffers from the modeled attack. The proposed index plays an important role in analyzing the ability of the real systems to resist unpredictable failures. Finally, based on the proposed community robustness index, a greedy algorithm is devised to mitigate the network attack. Experiments on three real network systems show that with minor changes in links the community robustness of networks can be greatly improved. The results also demonstrate that the community structures in the optimized networks remain practically unchanged compared with the original ones.
社区结构和鲁棒性是用于分析复杂系统功能的网络的两个重要属性。社区结构对于理解复杂系统的潜在功能至关重要，而鲁棒性对于保护复杂系统的功能免受恶意攻击不可或缺。当网络遭受不可预测的攻击时，其结构完整性会受到损害。早期研究聚焦于网络在遭受对节点或边的单级恶意攻击时节点结构或边结构的完整性。在本研究中，我们将对网络的攻击建模为两级有针对性的攻击。然后，我们提出一个社区鲁棒性指标来评估网络遭受建模攻击时社区结构的完整性。所提出的指标在分析实际系统抵抗不可预测故障的能力方面发挥着重要作用。最后，基于所提出的社区鲁棒性指标，设计了一种贪婪算法来减轻网络攻击。在三个真实网络系统上进行的实验表明，通过对链路进行微小改变，网络的社区鲁棒性可以得到极大提高。结果还表明，与原始网络相比，优化后网络中的社区结构实际上保持不变。
